I n many countries today, women make up half of medical students, and the number of female students choosing to pursue a career in medical imaging (radiology and nuclear medicine) is rising (1,2). However, the higher up the career ladder, the lower the proportion of women, a phenomenon known as the leaky pipeline (3). Compared with their male colleagues, women are underrepresented as authors, and leadership positions in medical imaging-either within institutions or within scientific organizations, committees, boards, or journals-are still dominated by men (4-6). Examples of challenges women face in general are maledominated cultures and networks, lack of female mentors, and explicit and implicit gender biases in recruitment, research allocation, outcomes of peer reviews, and citations (7-10). Working mothers face the well-described maternal wall bias, where maternal stereotyping and discrimination undermine their professional performance (11).Early reports on the effects of the COVID-19 pandemic on scientific research, all fields concerned, mention the deleterious effect the pandemic might have on the careers of parents working in science, and in particular on the scientific output of female researchers (12)(13)(14)(15)(16)(17). This is due to an unbalanced division of work, as women still perform the majority of household chores and care work, even in developed countries perceived as gender-egalitarian (18,19). Because schools and daycare facilities closed in many countries during the first COVID-19-related lockdown, the pandemic might thus eventually affect female career advancement, as the number and quality of publications in peer-reviewed journals one has authored are essential.The purpose of this study was to investigate whether the COVID-19 pandemic might have an impact on scientific publishing by female physicians in medical imaging. We performed a descriptive bibliometric analysis of female first and last authorship over the 3-month period corresponding to the first lockdown period in most countries due to the COVID-19 pandemic.Background: Early reports show the unequal effect the COVID-19 pandemic might have on men versus women engaged in medical research.Purpose: To investigate whether the COVID-19 pandemic has had an impact on scientific publishing by female physicians in medical imaging. Materials and Methods:The authors conducted a descriptive bibliometric analysis of the gender of the first and last authors of manuscripts submitted to the top 50 medical imaging journals from March to May 2020 (n = 2480) compared with the same period of the year in 2018 (n = 2238) and 2019 (n = 2355). Manuscript title, date of submission, first and last names of the first and last authors, journal impact factor, and author country of provenance were recorded. The Gender-API software was used to determine author gender. Statistical analysis comprised x 2 tests and multivariable logistic regression.

Purpose We investigated whether artificial intelligence (AI)-based denoising halves PET acquisition time in digital PET/CT. Methods One hundred ninety-five patients referred for [18F]FDG PET/CT were prospectively included. Body PET acquisitions were performed in list mode. Original “PET90” (90 s/bed position) was compared to reconstructed ½-duration PET (45 s/bed position) with and without AI-denoising, “PET45AI and PET45”. Denoising was performed by SubtlePET™ using deep convolutional neural networks. Visual global image quality (IQ) 3-point scores and lesion detectability were evaluated. Lesion maximal and peak standardized uptake values using lean body mass (SULmax and SULpeak), metabolic volumes (MV), and liver SULmean were measured, including both standard and EARL1 (European Association of Nuclear Medicine Research Ltd) compliant SUL. Lesion-to-liver SUL ratios (LLR) and liver coefficients of variation (CVliv) were calculated. Results PET45 showed mediocre IQ (scored poor in 8% and moderate in 68%) and lesion concordance rate with PET90 (88.7%). In PET45AI, IQ scores were similar to PET90 (P = 0.80), good in 92% and moderate in 8% for both. The lesion concordance rate between PET90 and PET45AI was 836/856 (97.7%), with 7 lesions (0.8%) only detected in PET90 and 13 (1.5%) exclusively in PET45AI. Lesion EARL1 SULpeak was not significantly different between both PET (P = 0.09). Lesion standard SULpeak, standard and EARL1 SULmax, LLR and CVliv were lower in PET45AI than in PET90 (P < 0.0001), while lesion MV and liver SULmean were higher (P < 0.0001). Good to excellent intraclass correlation coefficients (ICC) between PET90 and PET45AI were observed for lesion SUL and MV (ICC ≥ 0.97) and for liver SULmean (ICC ≥ 0.87). Conclusion AI allows [18F]FDG PET duration in digital PET/CT to be halved, while restoring degraded ½-duration PET image quality. Future multicentric studies, including other PET radiopharmaceuticals, are warranted.

BackgroundWith a constantly increasing number of diagnostic images performed each year, Artificial Intelligence (AI) denoising methods offer an opportunity to respond to the growing demand. However, it may affect information in the image in an unknown manner. This study quantifies the effect of AI-based denoising on FDG PET textural information in comparison to a convolution with a standard gaussian postfilter (EARL1).MethodsThe study was carried out on 113 patients who underwent a digital FDG PET/CT (VEREOS, Philips Healthcare). 101 FDG avid lesions were segmented semi-automatically by a nuclear medicine physician. VOIs in the liver and lung as reference organs were contoured. PET textural features were extracted with pyradiomics. Texture features from AI denoised and EARL1 versus original PET images were compared with a Concordance Correlation Coefficient (CCC). Features with CCC values ≥ 0.85 threshold were considered concordant. Scatter plots of variable pairs with R2 coefficients of the more relevant features were computed. A Wilcoxon signed rank test to compare the absolute values between AI denoised and original images was performed.ResultsThe ratio of concordant features was 90/104 (86.5%) in AI denoised versus 46/104 (44.2%) with EARL1 denoising. In the reference organs, the concordant ratio for AI and EARL1 denoised images was low, respectively 12/104 (11.5%) and 7/104 (6.7%) in the liver, 26/104 (25%) and 24/104 (23.1%) in the lung. SUVpeak was stable after the application of both algorithms in comparison to SUVmax. Scatter plots of variable pairs showed that AI filtering affected more lower versus high intensity regions unlike EARL1 gaussian post filters, affecting both in a similar way. In lesions, the majority of texture features 79/100 (79%) were significantly (p&lt;0.05) different between AI denoised and original PET images.ConclusionsApplying an AI-based denoising on FDG PET images maintains most of the lesion’s texture information in contrast to EARL1-compatible Gaussian filter. Predictive features of a trained model could be thus the same, however with an adapted threshold. Artificial intelligence based denoising in PET is a very promising approach as it adapts the denoising in function of the tissue type, preserving information where it should.

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